IDLab, Department of Information Technology Ghent University - imec, Technologiepark-Zwijnaarde 126, Ghent 9052, Belgium.
The Human Link, Grotesteenweg 93, Antwerp 2600, Belgium; University Psychiatric Centre Duffel, Stationsstraat 22, Duffel 2570, Belgium.
Comput Methods Programs Biomed. 2022 Oct;225:107077. doi: 10.1016/j.cmpb.2022.107077. Epub 2022 Aug 19.
Anxiety disorders are highly prevalent in mental health problems. The lives of people suffering from an anxiety disorder can be severely impaired. Virtual Reality Exposure Therapy (VRET) is an effective treatment, which immerses patients in a controlled Virtual Environment (VE). This creates the opportunity to confront feared stimuli and learn how to deal with them, which may result in the reduction of anxiety. The configuration of these VEs requires extensive effort to maximise the potential of Virtual Reality (VR) and the effectiveness of the therapy. Manual configuration becomes infeasible when the number of possible virtual stimuli combinations is infinite. Due to the growing complexity, acquiring the skills to truly master a VR system is difficult and it increases the threshold for psychotherapists to use such useful systems. We therefore developed a prototype of a supportive algorithm to facilitate the use of VRET in a clinical setting. This automatised system assists psychotherapists to use the wide range of functionalities without burdening them with technical challenges. Thus, psychotherapists can focus their attention on the patient.
In this paper both the prototype of the algorithm and a first proof of concept are described. The algorithm suggests environment configurations for VRET, tailored to the individual therapeutic needs of each patient. The system aims to maximise learning during exposure therapy for different combinations of stimuli by using the Rescorla-Wagner model as a predictor for learning. In a first proof of concept, the VE configurations suggested by the algorithm for three anonymised clinical vignettes were compared with prior manual configurations by two psychotherapists.
The prototype of the algorithm and a first proof of concept are described. The first proof of concept demonstrated the relevance and potential of the proposed system, as it managed to propose similar configurations for the clinical vignettes compared to those made by therapists. Nonetheless, because of the exploratory nature of the study, no claims can yet be made about its efficacy.
With the increasing ubiquity of immersive technologies, this technology for assisted configuration of VEs could make VRET a valuable tool for psychotherapists.
焦虑障碍在精神健康问题中极为普遍。患有焦虑障碍的人的生活可能会严重受损。虚拟现实暴露疗法（VRET）是一种有效的治疗方法，它将患者沉浸在受控的虚拟环境（VE）中。这为患者提供了面对恐惧刺激并学习如何应对它们的机会，从而可能降低焦虑水平。这些 VE 的配置需要大量的努力来最大限度地发挥虚拟现实（VR）的潜力和治疗的有效性。当可能的虚拟刺激组合数量无穷大时，手动配置变得不可行。由于复杂性不断增加，获得真正掌握 VR 系统的技能变得困难，并且增加了心理治疗师使用此类有用系统的门槛。因此，我们开发了一个支持算法的原型，以促进在临床环境中使用 VRET。这个自动化系统可以帮助心理治疗师在不承担技术挑战的情况下使用广泛的功能。因此，心理治疗师可以将注意力集中在患者身上。
本文描述了算法的原型和第一个概念验证。该算法根据每个患者的个体治疗需求为 VRET 建议环境配置。该系统旨在通过使用 Rescorla-Wagner 模型作为学习预测器，最大化不同刺激组合暴露治疗期间的学习效果。在第一个概念验证中，比较了算法为三个匿名临床病例建议的 VE 配置与两位心理治疗师的先前手动配置。
描述了算法的原型和第一个概念验证。第一个概念验证证明了所提出系统的相关性和潜力，因为它设法为临床病例提出了与治疗师提出的类似配置。然而，由于研究的探索性质，目前还不能对其疗效提出任何主张。
随着沉浸式技术的普及，这种辅助 VE 配置的技术可以使 VRET 成为心理治疗师的有价值的工具。
